The note provides an initial theoretical explanation of the way norm regularizations
may provide a means of controlling the non-asymptotic probability of False Dominance
classification for empirically optimal portfolios satisfying empirical Stochastic Dominance
restrictions in an iid setting. It does so via a dual characterization of the norm-constrained
problem, as a problem of Distributional Robust Optimization. This enables the use of
concentration inequalities involving the Wasserstein distance from the empirical distribu-
tion, to obtain an upper bound for the non-asymptotic probability of False Dominance
classification. This leads to information about the minimal sample size required for this
probability to be dominated by a predetermined significance level.